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混合智能反射面辅助的通信感知一体化:高能效波束成形设计

褚宏云 杨梦瑶 黄航 郑凌 潘雪 肖戈

褚宏云, 杨梦瑶, 黄航, 郑凌, 潘雪, 肖戈. 混合智能反射面辅助的通信感知一体化:高能效波束成形设计[J]. 电子与信息学报, 2024, 46(6): 2462-2469. doi: 10.11999/JEIT230699
引用本文: 褚宏云, 杨梦瑶, 黄航, 郑凌, 潘雪, 肖戈. 混合智能反射面辅助的通信感知一体化:高能效波束成形设计[J]. 电子与信息学报, 2024, 46(6): 2462-2469. doi: 10.11999/JEIT230699
CHU Hongyun, YANG Mengyao, HUANG Hang, ZHENG Ling, PAN Xue, XIAO Ge. Hybrid Reconfigurable Intelligent Surface Assisted Integrated Sensing and Communication: Energy Efficient Beamforming Design[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2462-2469. doi: 10.11999/JEIT230699
Citation: CHU Hongyun, YANG Mengyao, HUANG Hang, ZHENG Ling, PAN Xue, XIAO Ge. Hybrid Reconfigurable Intelligent Surface Assisted Integrated Sensing and Communication: Energy Efficient Beamforming Design[J]. Journal of Electronics & Information Technology, 2024, 46(6): 2462-2469. doi: 10.11999/JEIT230699

混合智能反射面辅助的通信感知一体化:高能效波束成形设计

doi: 10.11999/JEIT230699
基金项目: 国家自然科学基金(62102314),173计划技术领域基金(2022-JCJQ-JJ-0730),陕西省自然科学基金(2022JQ-635)
详细信息
    作者简介:

    褚宏云:女,讲师,硕士生导师,研究方向为智能超表面使能无线通信系统关键技术

    杨梦瑶:女,硕士生,研究方向为通信感知一体化

    黄航:男,高级工程师,研究方向为电子对抗

    郑凌:男,讲师,硕士生导师,研究方向为下一代网络体系架构、高性能网络与交换、人工智能算法及其FPGA实现

    潘雪:女,硕士生,研究方向为智能超表面信道估计

    肖戈:男,硕士生,研究方向为智能超表面波束形成

    通讯作者:

    杨梦瑶 myyang1@yeah.net

  • 中图分类号: TN929.5

Hybrid Reconfigurable Intelligent Surface Assisted Integrated Sensing and Communication: Energy Efficient Beamforming Design

Funds: The National Natural Science Foundation of China (62102314), The 173 Program for Technology (2022-JCJQ-JJ-0730), The Natural Science Foundation of Shaanxi Province (2022JQ-635)
  • 摘要: 能量效率(EE)是5G+/6G无线通信的重要设计指标,而智能反射面(RIS)被普遍认为是改善EE的潜在手段。不同于被动RIS,混合RIS由有源和无源元件组成,对来波移相的同时可放大信号强度,能够有效克服被动RIS引起的“乘性衰落”效应。鉴于此,该文提出一种混合RIS辅助通信感知一体化(ISAC)的下行链路传输系统。为探究数据传输速率与能耗之间的内在关联,该文以RIS辅助ISAC网络能量效率最大化为目标,在满足基站(BS)发射功率、波束图增益以及混合RIS功率和幅值约束的条件下,联合优化基站端的波束赋形和混合RIS的相移。为解决该复杂的分数规划问题,提出基于交替优化(AO)的算法来求解。为克服AO算法中引入辅助变量造成算法复杂度高的难题,利用耦合优化变量的关联,提出一种基于级联深度学习网络的求解算法。仿真结果表明,提出的混合RIS辅助ISAC方案在和速率、能效方面皆优于现有方案,且算法收敛速度快。
  • 图  1  混合RIS辅助的ISAC系统模型

    图  2  两阶段波束赋形网络结构图

    图  3  批量大小对损失的影响

    图  4  不同基站发射功率下的能量效率

    图  5  不同基站发射功率下的用户和速率

    图  6  不同RIS元件数目下的能量效率

    图  7  波束图

    算法1 算法整体流程
     初始化:变量$ {b^{(0)}} $、$ {\xi ^{(0)}} $、$ {{\boldsymbol{W}}^{(0)}} $和$ {{\boldsymbol{\theta }}^{(0)}} $
     迭代次数$ i = 1 $,最大迭代次数$ {I_{\max }} $
     (1)While$ {f_i} - {f_{i - 1}} \ge \varepsilon $或$ i \lt {I_{\max }} $do
     (2)根据式(11)更新拉格朗日对偶重组辅助变量$ {\xi ^{(i)}} $
     (3)根据式(13)更新二次变换辅助变量$ {b^{(i)}} $
     (4)利用CVX求解凸规划问题$ {{\text{P}}_{{\text{2-1}}}} $,更新优化变量$ {{\boldsymbol{W}}^{(i)}} $
     (5)利用CVX求解凸规划问题$ {{\text{P}}_{{\text{3}}}} $,更新优化变量$ {{\boldsymbol{\varphi}} ^{(i)}} $
     (6)更新能量效率$ \eta $,迭代次数$ i = i + 1 $
     (7)End While
    下载: 导出CSV

    表  1  部分仿真参数

    参数名称 符号 数值 参数名称 符号 数值
    BS发射天线数 $M$ 8 用户噪声功率(dBm) $\sigma _0^2$ –80
    用户数 $K$ 4 RIS噪声功率(dBm) $\sigma _c^2$ –70
    目标数 $L$ 2 Rice因子 $ \rho $ 10
    RIS元件总数 $N$ 256 最小波束图增益(dB) $ \varGamma $ 10
    RIS主动元件数 $T$ 64 目标方向($ ^\circ $) $ {\theta _1},{\theta _2} $ $ \pm 45 $
    RIS最大消耗功率(dBm) ${P_0}$ 10 能量放大系数 ${{a,b}}$ 0.8
    RIS有源元件放大系数(dB) $\alpha $ 10 惩罚系数 $ {\beta _1},{\beta _2},{\beta _3} $ 50
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-07-12
  • 修回日期:  2023-11-14
  • 录用日期:  2023-11-14
  • 网络出版日期:  2023-11-21
  • 刊出日期:  2024-06-30

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